Tag: invited speakers

Evan Seyfried ’16 shares the following summary of a talk given by Princeton’s Atif Mian this May to the UPF Department of Economics and Business. Follow Evan on Twitter @evanseyf

The bubble

In 2006, house prices in the U.S. reached their all-time peak. The S&P/Case-Shiller Housing Price Index had doubled in just eight years (not accounting for inflation).1 The year before, Robert Shiller (whose work on historical housing prices led to the creation of the Case-Shiller Index) had published an update to his book Irrational Exuberance warning that recent growth in housing prices was historically unprecedented—he argued that houses were wildly overpriced and would likely revert back to a relatively constant historical value in the long run.2 His research showed that if you looked at real prices (inflation-adjusted) in the U.S. housing market prior to the early 2000s bubble, you would find that prices have not changed much since 1890!

Figure 1. Case-Shiller Home Price Index from 1890-2014. Values are real (corrected for inflation) and are set relative to 1890 prices (which is defined as 100). Source: Data from Robert Shiller, graph from The Atlantic3

The frenzy of the early 2000s finally caught up with lenders, homeowners, and investors, who began to doubt the continued rise of house prices. In late 2005, with interest rates rising, a growing number of homeowners with Adjustable-Rate Mortgages (ARM) began to default on their mortgages. Finally, by the end of 2006 the housing bubble began to collapse under its own weight, and the shockwaves ripped through the financial sector—which had bet heavily on the U.S. housing market through mortgage-backed securities and newer exotic financial instruments. French bank BNP Paribas, on August 7, 2007, famously suspended withdrawals from its investment funds associated with subprime mortgages, a move that triggered a shadow banking run, and is often considered the official start of the financial crisis—when the housing market instability truly began to upend the financial sector. What followed was the most severe financial crisis since the Great Depression and a long recession for the rest of the U.S. economy.

But there is still much to be learned about the interaction of the housing bust (leaving many homeowners with very high debt compared to their assets), the crisis in the financial sector (wherein banks have been generally unwilling to either extend new credit or restructure existing loans), and the continuing economic malaise in the U.S. and other economies around the world.

From the housing bubble to household debt

A great deal of Princeton economist Atif Mian’s research—much of it in collaboration with University of Chicago economist Amir Sufi—has studied these interactions, exploring the fallout from the housing bubble in the U.S. and the subsequent “debt overhang.”

What is household debt overhang?

Imagine a family owes $200,000 on their mortgage. If the market crashes and the house value suddenly declines to $180,000, then the family now owes $20,000 more than the value of their house. Thus, even if the family chooses to sell the house, they will not be able to pay back the mortgage in full. This is also called being “underwater” on a mortgage. In the context of all household finances, debt overhang is a similar concept to being underwater, and refers to the amount of indebtedness of a family beyond the value of their assets, taking into account their anticipated income. Debt overhang makes a household unattractive to lenders (both for new loans and for refinancing old loans), because they do not have any collateral that is not already used to cover existing debt.

Note that household debt is treated separately from other private sector debt (mainly non-financial firm debt), and shows notably different dynamics. All of Atif Mian’s research mentioned here focuses specifically on household debt.

In 2013, Mian published evidence that poorer families who were highly leveraged in the housing market reacted very sharply to the loss of wealth when their homes depreciated following the housing bust. Because their marginal propensity to consume out of housing wealth (how much families spend knowing that they have a certain amount of wealth in their house to fall back on) is higher than for middle- or upper-income families, their consumption dropped disproportionately in the years after the bubble.4 Of course, at the individual level this behavior is rational, but at the national level low consumption growth in a demand-constrained economy has created a negative feedback loop of lower job growth, lower income growth, and a further drop in consumption growth.

One of the takeaways from this body of research is that governments and international finance organizations need to do a better job of properly accounting for how private sector debt affects consumption. Optimistic forecasts for recovery from the 2008-2010 Great Recession did not sufficiently account for depressed demand as homeowners and those with credit card and student debt eschewed consumption to deleverage themselves. In a comment on Karen Dynan’s research on household debt overhang and consumption, Mian wrote: “… macroeconomic policy in a world where consumption is driven by debt overhang needs to be seen through its implications for the net worth of the borrowing households.”5

Imperfect instruments?

But Mian also wanted to take these insights from the Great Recession and ask more fundamental questions about private debt and predictions of economic growth: Was consumption affected similarly affected during other periods of high household debt? Do we see similar household debt effects in other countries? If so, how does this extra drag on consumption affect how economists forecast economic growth?

Atif Mian. Photo: The New York Times

Mian recently gave a lecture at the Universitat Pompeu Fabra in Barcelona, presenting the findings from his attempt to answer those questions. (The working paper, coauthored with Amir Sufi and Emil Verner, is available from the National Bureau of Economic Research.6 ) They took a sample of 30 countries (mostly advanced economies) and compiled private debt data back to 1960. Then they identified shocks to household credit and looked at the relationship between those shocks and subsequent GDP growth. (In this context, shocks should be thought of as sudden increases in the availability of credit.)

Initially they found that high growth in household credit was predictive of subsequent low GDP growth. But they needed to identify the nature of those credit shocks to find possible causal channels. According to Mian they wanted to “rule out demand-driven shocks.” Demand-driven shocks come from the consumer side and could be an increase in the use of credit to smooth lifetime consumption, or as an “insurance effect” to get liquidity today due to uncertainty or an expectation of economic shocks tomorrow. On the other hand, a supply-driven credit shock would be banks extending more and more credit due to government policy changes or financial innovation.

The first demand-driven possibility is relatively simple to disprove. Because the Permanent Income Hypothesis suggests that households borrow today in the expectation of higher future income, the fact that household debt increases should be indicative of economic growth. As mentioned before, Mian, Sufi, and Verner find the exact opposite relationship. The second demand-driven possibility is unlikely because much of the growth in household debt across all the countries in the survey is in mortgage debt, which is generally not taken on to provide liquidity.

Next, they looked into the supply-driven credit shock mechanism and tried to find a way to overcome the presumably endogenous relationship between credit supply shocks and subsequent lower GDP growth. The mechanism must explain why people borrow in the first place, especially what causes them to over-borrow (what Mian calls an “aggregate demand externality”—an effect that spills over to other borrowers), and explain why excessive borrowing actually leads to a decline in real output (what Mian calls “macro frictions” that generate the slowdown, such as monetary policy and “wage rigidity”). As the authors write in the paper: “The key ingredient in this model is an aggregate demand externality that is not properly internalized by borrowing households at the time they make their borrowing decision.”

Two problems remained. First, the authors had to come up with a measure of “credit supply shocks” that could apply to dozens of different countries. Second, they had to choose a measure that could help identify the causal relationship, not just the correlation. Their solution was to use one measure for the U.S. (share of debt issuance by risky firms) and a simpler one for non-U.S. economies (the spread of sovereign debt yields compared to equivalent U.S. Treasury notes). According to Mian, these are “not instruments in the usual sense of the word” (which must satisfy the requirements of independence from the outcome variable and relevance to the explanatory variable). Rather, they are “imperfect instruments” (see Nevo and Rosen, 2012.7 for more information) and, per Mian, “as long as we can sign the covariance of the instrument, we can partially identify the range in which the coefficient lies.” In other words, because these proxies for credit supply shocks typically signify expectations of good times, then if we see that they actually predict bad times, we can at least identify a range of values for how strong the link is between an increase in household debt and subsequent low growth.

The methodology is admittedly complex, and audience members had some reservations about how the authors had dealt with household debt (particularly since household debt is mostly mortgage debt). One audience member suggested that housing bubbles could be the main driver of subsequent low growth, with the extension of credit simply a side effect. Mian acknowledged that he cannot outright reject this concern, but added that the results are robust to controls for house prices, so the bubbles should be controlled for. Another audience member suggested that this could be tested for if the data set included any countries which had seen a credit boom with no attendant housing bubble. There are, in fact, some countries in the data set, but, as Mian stressed, there was not enough of a subsample for a strong statistical test of this hypothesis.

Onward to global growth!

After presenting the “within country” results—showing that household credit supply shocks tended to lead to lower growth in the five or so years following—Mian pivoted to the global portion of the paper. The goal here was to establish the spillover effects of these credit supply shocks among different countries. Sure enough, Mian stated that “the global cycle is more destructive” due to financial spillovers between countries. Because the growth slowdown in a given country after the credit shock leads to a reduction in imports, the problem is transferred to that country’s trading partners. Furthermore, the effects are exacerbated by “macro frictions,” especially in countries that employ fixed exchange rate regimes, borrow primarily in foreign currency, and are near the zero interest rate lower bound (although recently the zero interest rate bound has been proving not to be much of a hard bound after all). Figure 2 shows these global aggregate effects.

Mian stressed that these dynamics between debt and growth, especially the global ones, should be seen as relatively recent (“last-forty-years effects”) side effects of globalization and the financialization of household debt. He concluded that governments must respond to these powerful forces with targeted macroprudential policies, and forecasters at organizations like the IMF and OECD must learn to better account for household debt in their growth projections.

Lecture summary by Tuomas Kari ’16 (Master’s in International Trade, Finance, and Development)

The former Chief Economist of the World Bank and member of Barcelona GSE Scientific Council Justin Yifu Lin visited Barcelona GSE on May 2nd to give a special talk to the Master students on a new approach to development policy, titled “New Structural Economics: The Third Wave of Development Thinking”. Professor Lin, who currently teaches at the National School of Development at the University of Beijing, outlined the history of development economics and its shortcomings. The goal of the lecture was to derive lessons for optimal policy and then expand upon the idea of new structural economics, the approach Prof. Lin himself advocates.

Structuralism and neoliberalism

Prof. Lin divided the history of development into two time periods: structuralism that was dominant from 1950 to the 1980s, and neoliberalism that has been the main viewpoint up to this day. Structuralism tended to assume that there were market failures that needed to be corrected with industrial policy, such as import substitution. The failure of these policies is well documented as the government-subsidized industries rarely survived at global markets and distorted the countries’ economies. Neoliberalist reaction emphasized deregulation to rid the economy of rent seeking and liberalization to let markets determine the allocation of resources. But this too failed in developing countries to reach steady growth. Often, liberalization led to the collapse of entire sectors, high unemployment and subsequent political unrest.

The main exception to these consensus policies throughout the last half a century have been the East Asian Tigers, Hong Kong, Singapore, South Korea and Taiwan, countries that followed a dual track of capitalist and state-directed policies and achieved unmatched growth rates. As these countries were initially too poor to afford expensive subsidies to heavy industry, they promoted production lower in the value chain, and even then only by piece-meal measures. According to Prof. Lin, this lack of better options guided the Tigers to good policies by accident.

Professor Lin delivered the Barcelona GSE Lecture at Banc Sabadell later the same day to the entire BGSE community.

Economic growth as a result of structural transformation

New structural economics is an attempt to study the determinants of economic structure and its evolution using neoclassical methods. Prof. Lin starts from the hypothesis that economic structure is endogenous to the country’s endowments and optimal policy guides the economy to activities where it enjoys comparative advantage. If a country attempts to transform its economy to activities other than those that utilize its endowments, this will only result in distortions, breaking down of market mechanisms and rent seeking. Optimal policy must start from the development of endowments (capital stock, human capital etc.) and only after try to deal with the production structure. As economic growth is ultimately a result of structural transformation, Prof. Lin argued that governments must engage in first building up the necessary endowments and then using industrial policy to help firms enter into business.

The preconditions for economic growth are having a functioning market economy efficiently allocate resources across sectors and firms, and a facilitating state that provides transitional support for firms entering and exiting the market and liberalizing the economy gradually using discretion. Lin claimed would lead to competitiveness, openness to trade, and strong fiscal and external accounts, which allow the economy to avoid crises and engage in countercyclical policies. Another benefit would be high returns to investment that provide incentives to save.

Room for more economic research

Prof. Lin promoted the setting up of Special Economic Zones to allow firms to do business free from distortions and also work as laboratories for the government to see what the comparative advantages of the economy are. He ended the lecture by proposing the development of theoretical models capable of explaining these dynamics as a fruitful avenue for the future economists in the audience.

Lecture summary by Evan Seyfried ’16 (Master’s in Economics of Public Policy). Above, the author talks with Robert Lucas after the lecture.

The modish Banco Sabadell Lecture Hall, overlooking grand, prosperous Avinguda Diagonal, is filled to capacity this Thursday evening. Nobel laureate economist Robert Lucas is here to present the 34th Barcelona GSE Lecture, and the GSE community is eagerly anticipating the talk.

“What was the Industrial Revolution?”

The topic would have seemed almost trite in less-skilled hands. Lucas, however, over the past decade has focused his talents on exploring economic models that might explain rapid industrialization like that of the United Kingdom starting in the late 18th century. He views the rise of urbanization and industrialization through the lens of economist Gary Becker’s theory of population fertility and couples it with a human capital growth model.

This talk draws heavily from Lucas’s recent research on human capital and economic growth1, the diffusion of the Industrial Revolution2, and a rejection of the “great men” hypothesis of economic progress3.

The central hypothesis of his lecture tonight is, essentially, that of his 2015 paper on economic growth, with its blissfully short abstract:

“This paper describes a growth model with the property that human capital accumulation can account for all observed growth. The model is shown to be consistent with evidence on individual productivities as measured by census earnings data. The central hypothesis is that we learn more when we interact with more productive people.”1

From this basis, Professor Lucas presents his most recent work on the topic. He begins with a graph—how else would an economist begin any lecture?—showing the striking relationship between a country’s prosperity (measured in GDP per capita) and the share of its population employed in agriculture.

Why is the relationship between these two variables so consistent? Later in the lecture, Lucas will develop his model based on a “dual economy” of low-skilled agricultural workers and various levels of skilled urban dwellers.

But first, a little history.

“Macroeconomics’ finest hour.” (A brief historical digression.)

Thomas Robert Malthus, English cleric and scholar, became famous (and, to some, infamous) when he published “An Essay on the Principle of Population” in 1798. The essay neatly distilled a framework for pre-industrial population dynamics:

“Yet in all societies, even those that are most vicious, the tendency to a virtuous attachment is so strong that there is a constant effort towards an increase of population. This constant effort as constantly tends to subject the lower classes of the society to distress and to prevent any great permanent amelioration of their condition.”4

Its publication led to a massive controversy that rapidly spread across the landscape of political economy. Although Malthus’s work was not nearly as apocalyptic as his deriders asserted, it still pointed out uncomfortable truths about the seemingly unrelenting misery of the lower classes, even in “advancing” nations.

A century and a half later, economist Gary Becker took up the Malthusian mantle with his seminal work, “An Economic Analysis of Fertility,” a study of the dynamics of family planning and income. Becker explicitly acknowledged his debt to Malthus: “[…] Malthus’ famous discussion was built upon a strongly economic framework; mine can be viewed as a generalization and development of his.”5 Becker’s further research concluded that viewing fertility as a result of marginal-cost/marginal-benefit decisions is a satisfying way to explain the phenomenon of high-income families voluntarily lowering their fertility rates. His framework implies that families with more human capital invest more resources in fewer children.

Professor Lucas calls the Malthusian insight and the subsequent robust debate among political economists of the day “macroeconomics’ finest hour.”

The Path Off the Farm: What Is an Industrial Revolution?

Lucas now presents his synthesis: Becker’s fertility model combined with Lucas’s own human capital model, both placed in the context of the urban-agricultural dual economy.

Like Becker, Lucas’s model has parents view their children as “durable goods” that yield a “psychic utility” but also impose costs. As families move up the socioeconomic ladder, they make different decisions regarding investment in the “quality” of the children (everything from time spent teaching, to money invested in tutors and private schools). Over time, the quantity/quality balance leads to lowering fertility among higher socioeconomic classes.

In the dual-economy framework, rural (landless or small proprietor) farmers are pushed by wage considerations to move into dynamic urban environments as low-skilled workers. At first, with no wealth to invest in their children, they make “quantity” a priority over “quality.” Over generations, however, there is a tipping point where a given family has accumulated enough resources to make meaningful human-capital investments in their children. Once this occurs, they can now move up to the higher-skilled tiers of society. Crucially, it is this accumulation, not of wealth, but of human capital, that drives further growth in Lucas’s model. The speed at which these changes occur depends on the magnitude of “interactions” in society: how often and to what degree people engage with one another in productive exchanges—anything from academic discussions to business deals.

A key mechanism in the model is that economic growth itself is what allows the low-skilled workers, coming from the farms, to dependably get better and better jobs over time. Thus, the dynamic is self-reinforcing as more rural workers move to the cities.

When considering the Industrial Revolution, we can appreciate how natural it would be to dismiss the intangible, fuzzy concept of “human capital” and only focus on material capital: factories, infrastructure, mines, etc. But if we view the Industrial Revolution with Lucas’s model in mind, we can at the very least see that Lucas’s statement from his 2015 paper—”human capital accumulation can account for all observed growth”1—is quite plausible.

Later in the same paper, Lucas asserts: “The contribution of human capital accumulation to economic growth deserves a production function of its own.” 1 In the model Lucas has presented tonight, he answers his own demand. There is, indeed, a “production function” for human capital, and when it is coupled to a fertility model, it can show the dynamics of rapid urbanization and economic growth. In other words, it can model an industrial revolution.

To use Lucas’s own words from the lecture: “We used to think of the Industrial Revolution as factories and coal, but I think the main consequence was the emergence of the bourgeoisie who are just creating things out of nothing, generating wealth and production.”

Postscript

What does this all mean? What are the implications of this model in 2016?

We go back to the graph showing the relation between GDP per capita and share of population in agricultural work. Lucas mentions how many areas of the world are still in the upper left portion of the graph—poor, agricultural, unskilled-labor-intensive economies. According to Lucas, we must not be deluded into thinking that a pastoral lifestyle is something to be preserved at the cost of indefinite poverty. Lucas states, “The idea that you can prettify this lifestyle is just plain wrong.” Rather, we have a collective interest in the flourishing of all people around the world, and we have emerging evidence that investment in human capital, coupled with smart urbanization, is one of the best ways to achieve it.

The questions following the lecture are—as expected from economists—pointed. In response to questions about the refugee and economic migrant crisis in the EU, Lucas denies that his model says anything specific about it, but states emphatically that he supports immigration in general. Finally, when asked about the prospects of continued economic growth, given recent anxiety about economic stagnation, Lucas responds that since the Industrial Revolution we have seen stable growth unlike any period before in recorded history. He believes that growth will continue, as capitalism reinvents itself yet again, this time for the information age—though he admits that “flush toilets are way more important than Facebook.”

With that, the lecture concludes, and the lucky attendees weigh the expected utility of waiting in line to speak with the most influential living economist against the expected utility of beating the rush to the cava and jamón ibérico at the reception. The gears of the market keep turning.

During the 31st Barcelona GSE Lecture, Professor Jonathan Levin, Holbrook Working Professor of Price Theory at Stanford University, discussed the advantages and challenges of using Internet commerce data for empirical research in economics.

Seller Experience – Let real online sellers run experiments for us

Economic research has long relied on public governmental institutions and organisations to collect empirical data, which are reliable but expensive. Data on the Internet could potentially be an alternative source as it can reduce the cost of varying parameters. However, it is also challenging to isolate specific effects as customization of products and services raises concerns about selection and endogeneity.

Prof. Levin and his collaborators found that the millions of listings on eBay could potentially serve as millions of small experiments with different seller choices. In collaboration with eBay, US data allowed them to run fixed effect regressions exploiting within-experiment variation in prices, fees, displays, and other parameters.

Auction and Demand – Never start with an intermediate price

The buyers market is very competitive. If an auction starts at a low price, the item will be highly sellable and the market will drive it to around 80% of its value. Starting at a high price will make the item harder to sell, but increases the probability of ending with a higher price. The resulting estimated demand curve was convex, which implies that for a seller to start an auction with either a low or high price is more profitable than an intermediate price.

As second example, Prof . Levin argued that the Internet market could be used to test behavioural hypotheses about consumers. By looking at the multiple auction listings with different (flat rate) shipping fees, their analysis suggests that people prefer free shipping so much that they are willing to pay a higher price for the goods with free shipping even if there is an equal good with lower total price including shipping costs.

By analysing Internet commerce data from 2003 to 2012, they observed the sellers’ learning curve and concluded that the demand for online auction has declined. One interpretation of this development could be that consumers grew accustomed to increasingly instant purchase options and tend to spend more time on other online activities thus losing the attention for online auction.

Sales tax – a problem across US states

In the United States there exists a sales tax of 8.875% on average among states for within state sales. It does not come as a surprise that the payment of this tax is viewed as a negative additional charge which online often appears just towards the end of the purchasing process. To analyse the impact of the ‘tax surprise’ Prof. Levin estimates the tax sensitivity by comparing purchase rates.

Prof. Levin’s research finds that both consumers and sellers are aware of this perceived “extra charge” and the fact that the tax does not apply on out-of-state sales. Their analysis finds two consumer trends: first there exists a preference for goods bought in geographic proximity; second, the rising item-level substitution which means the consumer chooses to buy the item from an out-of-state seller to avoid the tax. These two trends seem to form a paradox.

Professor Nezih Guner (right) with our speaker

On the seller side, Prof. Levin illustrates the case of Amazon and the location of their distribution centres. Amazon takes advantage of California’s geographic shape by locating its distribution centres just outside state lines while keeping delivery times short. This results in being able to cater to consumers in California without paying the sales tax.

Since online shopping is a growing trend, taxation legislation changes have potentially great impact. Based on the data used, a 1% increase in current sales tax decreases online sales by 1.5-2.0% but increase online home-state sales by 3-4%; alternatively switching to national tax collection of internet purchases would decrease online sales by 12%.

Professor Levin’s presentation concluded that new large-scale data offers an opportunity to assess microeconomic theories of behaviour and market operation. The presentation was followed by a Questions & Answers session on data quality, auction information asymmetry and lessons learned from use of auctions. For Yi-Ting Kuo and Hugo Kaminski it has been an insightful experience to listen to Professor Levin’s talk presented with support from Banco Sabadell.

If you are interested to know more about the lecture, you may view it here: